EMR Analysis Reveals Significant Predictors of Fibromyalgia Diagnosis


Noting that it takes an average of 5 years before a fibromyalgia patient receives a diagnosis, researchers conducted a retrospective data analysis to identify significant variables that may enable earlier detection and diagnosis of fibromyalgia.

Because fibromyalgia is associated with a number of symptoms and comorbidities, it takes an average of 5 years before a patient with the chronic pain condition receives a diagnosis. In an effort to reduce that considerable waiting period, researchers from Cedars-Sinai Medical Center in Los Angeles and Pfizer Inc. in New York City conducted a retrospective data analysis to identify significant variables that may enable earlier detection and diagnosis of fibromyalgia.

For their “Identifying Predictors of a Fibromyalgia Diagnosis” poster presented at the American College of Rheumatology 2013 Annual Meeting in San Diego, CA, Cedars-Sinai Bone Health Center Medical Director Stuart Silverman, MD, FACP, FACR, and 3 Pfizer employees compared electronic medical records (EMR) of 2,823 adult patients who received an initial fibromyalgia diagnosis in 2011 — indicated by the ICD-9 code for unspecified myalgia and myositis — to 210,495 adult patients who weren’t diagnosed with fibromyalgia, but whose EMR had at least one diagnostic code for a pain condition in that same year.

According to the investigators, both participant groups shared a mean age of 51.4 years old. However, 74 percent of the fibromyalgia population was female, compared to 60.4 percent of the control group, and there was also a higher proportion of Caucasian patients in the fibromyalgia cohort.

Examining differences in healthcare resource use between the two groups, Silverman and his research team found that “the mean number of opioids prescribed was twice as high in the fibromyalgia cohort (1.4 versus 0.7; P< 0.0001), and prescription pain medications were also significantly higher (3.1 versus 1.7; P< 0.0001), as was the mean number of overall prescriptions (16.9 versus 11.6; P< 0.0001).” Thus, “the number of pain medication prescriptions written other than opioids was the first variable entered into the model, and the associated odds ratio (OR) of 1.03 indicates that an increases of 1 pain medication increases the risk of fibromyalgia by 3%,” the poster authors wrote.

But pain prescriptions weren’t as significantly associated with fibromyalgia as chronic bladder inflammation, since the authors discovered that “the odds of an individual with interstitial cystitis being diagnosed with fibromyalgia is 3.15-fold higher than the odds of an individual without interstitial cystitis being diagnosed with fibromyalgia.”

While considering other comorbid conditions, the researchers noted that “in addition to a greater prevalence of musculoskeletal pain conditions among the fibromyalgia cohort relative to controls, there was also an approximate two-fold greater prevalence of most mental disorders, including anxiety and depression, as well as sleep disorders.”

Even though considerably more hospitalizations occurred in the fibromyalgia group, the number of hospitalizations actually had “a lower OR (0.76) for fibromyalgia after adjustment for covariates,” the authors wrote.

In total, the investigators identified 17 variables as predictors of a fibromyalgia diagnosis code, including the presence of irritable bowel syndrome (IBS) and fatigue.

“Several demographic and clinical variables were shown to be significant predictors of a fibromyalgia diagnosis among subjects who had diagnosis codes for common pain conditions. While some of these variables are consistent with the known epidemiology and patient-reported symptomatology of fibromyalgia, others were identified based on clinical information captured using EMR,” the authors concluded. “These results suggest analysis of EMR data can help identify variables associated with fibromyalgia in a real-world setting and may inform earlier identification of fibromyalgia patients.”

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